Dexter  Goodwin

Dexter Goodwin

1615000294

I’m not a data scientist but made a COVID mask detector with Google AutoML and React 

Using Google AutoML and React, I was able to set up a client-side object detection app, without any custom model code.

Neural-network-based object detection is a powerful technique that’s getting easier and easier to take advantage of. With Google’s  Cloud AutoML computer vision service (as well as similar services like Microsoft’s  Custom Vision), it’s now simple and cheap to train a powerful object detection model and deploy it as a client-side React app. And best of all, you don’t need to hire a data scientist to do it — the model training is code-free, so any application developer can train a model and focus on doing what they do best, which is building useful and fun applications!

Given the current state of the world with COVID-19, I thought it would be an interesting test case to try building a mask detector — something that could take a video stream, and report back on the locations of people in a frame who are wearing masks, and of those who aren’t. This could potentially be pretty useful to deploy in businesses trying to enforce mask mandates, and ride sharing services are already using something similar to check that drivers and riders are wearing masks.

This seemed like a great idea, except for the small detail that I didn’t actually know how to do that. Luckily I work at  AE Studio, where we take an Agile approach to building both traditional software applications and data science solutions for our clients. So I talked with AE’s head of data science,  Mr. Deep Learning himself, Ed Chen, to help me figure out what the simplest MVP could be.

What we found was that it’s now surprisingly simple for a single developer to build a high-quality on-device object detection system, without any special knowledge or large data sets. This app doesn’t snitch on anyone — it keeps all data on the client, and reacts when it detects a masked or unmasked face.

If you don’t want to peek behind the curtain, then feel free to skip ahead to check out our full-fledged mask detector at  doctormasky.com. Or, if you want to skip the explanations and jump into the code, the repo is on GitHub. Otherwise, read on!

End-to-end workflow

The end-to-end workflow for building a client-side object detector goes like this:

  1. Sign up for a Google AutoML account
  2. Find example images of the objects you want to detect. You can find these online, with a public dataset, or by taking them yourself.
  3. Upload the images to a Google Storage bucket, and label the dataset by drawing bounding boxes around the objects in the images.
  4. Google then uses that labeled data to create a model.
  5. You can deploy that model as endpoint to send images to. Or in this case, you can export that model to another Google Storage bucket, and use it for on-device detection within a webapp.

#automl-vision #react #object-detection #typescript

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I’m not a data scientist but made a COVID mask detector with Google AutoML and React 
Autumn  Blick

Autumn Blick

1598839687

How native is React Native? | React Native vs Native App Development

If you are undertaking a mobile app development for your start-up or enterprise, you are likely wondering whether to use React Native. As a popular development framework, React Native helps you to develop near-native mobile apps. However, you are probably also wondering how close you can get to a native app by using React Native. How native is React Native?

In the article, we discuss the similarities between native mobile development and development using React Native. We also touch upon where they differ and how to bridge the gaps. Read on.

A brief introduction to React Native

Let’s briefly set the context first. We will briefly touch upon what React Native is and how it differs from earlier hybrid frameworks.

React Native is a popular JavaScript framework that Facebook has created. You can use this open-source framework to code natively rendering Android and iOS mobile apps. You can use it to develop web apps too.

Facebook has developed React Native based on React, its JavaScript library. The first release of React Native came in March 2015. At the time of writing this article, the latest stable release of React Native is 0.62.0, and it was released in March 2020.

Although relatively new, React Native has acquired a high degree of popularity. The “Stack Overflow Developer Survey 2019” report identifies it as the 8th most loved framework. Facebook, Walmart, and Bloomberg are some of the top companies that use React Native.

The popularity of React Native comes from its advantages. Some of its advantages are as follows:

  • Performance: It delivers optimal performance.
  • Cross-platform development: You can develop both Android and iOS apps with it. The reuse of code expedites development and reduces costs.
  • UI design: React Native enables you to design simple and responsive UI for your mobile app.
  • 3rd party plugins: This framework supports 3rd party plugins.
  • Developer community: A vibrant community of developers support React Native.

Why React Native is fundamentally different from earlier hybrid frameworks

Are you wondering whether React Native is just another of those hybrid frameworks like Ionic or Cordova? It’s not! React Native is fundamentally different from these earlier hybrid frameworks.

React Native is very close to native. Consider the following aspects as described on the React Native website:

  • Access to many native platforms features: The primitives of React Native render to native platform UI. This means that your React Native app will use many native platform APIs as native apps would do.
  • Near-native user experience: React Native provides several native components, and these are platform agnostic.
  • The ease of accessing native APIs: React Native uses a declarative UI paradigm. This enables React Native to interact easily with native platform APIs since React Native wraps existing native code.

Due to these factors, React Native offers many more advantages compared to those earlier hybrid frameworks. We now review them.

#android app #frontend #ios app #mobile app development #benefits of react native #is react native good for mobile app development #native vs #pros and cons of react native #react mobile development #react native development #react native experience #react native framework #react native ios vs android #react native pros and cons #react native vs android #react native vs native #react native vs native performance #react vs native #why react native #why use react native

Osiki  Douglas

Osiki Douglas

1620127560

Data Scientist Creates Python Script To Track Available Slots For Covid Vaccinations

Bhavesh Bhatt, Data Scientist from Fractal Analytics posted that he has created a Python script that checks the available slots for Covid-19 vaccination centres from CoWIN API in India. He has also shared the GitHub link to the script.

The YouTube content creator posted, “Tracking available slots for Covid-19 Vaccination Centers in India on the CoWIN website can be a bit strenuous.” “I have created a Python script which checks the available slots for Covid-19 vaccination centres from CoWIN API in India. I also plan to add features in this script of booking a slot using the API directly,” he added.

We asked Bhatt how did the idea come to fruition, he said, “Registration for Covid vaccines for those above 18 started on 28th of April. When I was going through the CoWIN website – https://www.cowin.gov.in/home, I found it hard to navigate and find empty slots across different pin codes near my residence. On the site itself, I discovered public APIs shared by the government [https://apisetu.gov.in/public/marketplace/api/cowin] so I decided to play around with it and that’s how I came up with the script.”

Talking about the Python script, Bhatt mentioned that he used just 2 simple python libraries to create the Python script, which is datetime and requests. The first part of the code helps the end-user to discover a unique district_id. “Once he has the district_id, he has to input the data range for which he wants to check availability which is where the 2nd part of the script comes in handy,” Bhatt added.

#news #covid centre #covid news #covid news india #covid python #covid tracing #covid tracker #covid vaccine #covid-19 news #data scientist #python #python script

Siphiwe  Nair

Siphiwe Nair

1620466520

Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Java Questions

Java Questions

1599137520

50 Data Science Jobs That Opened Just Last Week

Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

In this article, we list down 50 latest job openings in data science that opened just last week.

(The jobs are sorted according to the years of experience r

1| Data Scientist at IBM

**Location: **Bangalore

Skills Required: Real-time anomaly detection solutions, NLP, text analytics, log analysis, cloud migration, AI planning, etc.

Apply here.

2| Associate Data Scientist at PayPal

**Location: **Chennai

Skills Required: Data mining experience in Python, R, H2O and/or SAS, cross-functional, highly complex data science projects, SQL or SQL-like tools, among others.

Apply here.

3| Data Scientist at Citrix

Location: Bangalore

Skills Required: Data modelling, database architecture, database design, database programming such as SQL, Python, etc., forecasting algorithms, cloud platforms, designing and developing ETL and ELT processes, etc.

Apply here.

4| Data Scientist at PayPal

**Location: **Bangalore

Skills Required: SQL and querying relational databases, statistical programming language (SAS, R, Python), data visualisation tool (Tableau, Qlikview), project management, etc.

Apply here.

5| Data Science at Accenture

**Location: **Bibinagar, Telangana

Skills Required: Data science frameworks Jupyter notebook, AWS Sagemaker, querying databases and using statistical computer languages: R, Python, SLQ, statistical and data mining techniques, distributed data/computing tools such as Map/Reduce, Flume, Drill, Hadoop, Hive, Spark, Gurobi, MySQL, among others.


#careers #data science #data science career #data science jobs #data science news #data scientist #data scientists #data scientists india

Dexter  Goodwin

Dexter Goodwin

1615000294

I’m not a data scientist but made a COVID mask detector with Google AutoML and React 

Using Google AutoML and React, I was able to set up a client-side object detection app, without any custom model code.

Neural-network-based object detection is a powerful technique that’s getting easier and easier to take advantage of. With Google’s  Cloud AutoML computer vision service (as well as similar services like Microsoft’s  Custom Vision), it’s now simple and cheap to train a powerful object detection model and deploy it as a client-side React app. And best of all, you don’t need to hire a data scientist to do it — the model training is code-free, so any application developer can train a model and focus on doing what they do best, which is building useful and fun applications!

Given the current state of the world with COVID-19, I thought it would be an interesting test case to try building a mask detector — something that could take a video stream, and report back on the locations of people in a frame who are wearing masks, and of those who aren’t. This could potentially be pretty useful to deploy in businesses trying to enforce mask mandates, and ride sharing services are already using something similar to check that drivers and riders are wearing masks.

This seemed like a great idea, except for the small detail that I didn’t actually know how to do that. Luckily I work at  AE Studio, where we take an Agile approach to building both traditional software applications and data science solutions for our clients. So I talked with AE’s head of data science,  Mr. Deep Learning himself, Ed Chen, to help me figure out what the simplest MVP could be.

What we found was that it’s now surprisingly simple for a single developer to build a high-quality on-device object detection system, without any special knowledge or large data sets. This app doesn’t snitch on anyone — it keeps all data on the client, and reacts when it detects a masked or unmasked face.

If you don’t want to peek behind the curtain, then feel free to skip ahead to check out our full-fledged mask detector at  doctormasky.com. Or, if you want to skip the explanations and jump into the code, the repo is on GitHub. Otherwise, read on!

End-to-end workflow

The end-to-end workflow for building a client-side object detector goes like this:

  1. Sign up for a Google AutoML account
  2. Find example images of the objects you want to detect. You can find these online, with a public dataset, or by taking them yourself.
  3. Upload the images to a Google Storage bucket, and label the dataset by drawing bounding boxes around the objects in the images.
  4. Google then uses that labeled data to create a model.
  5. You can deploy that model as endpoint to send images to. Or in this case, you can export that model to another Google Storage bucket, and use it for on-device detection within a webapp.

#automl-vision #react #object-detection #typescript